As of 2022, solar energy provided about 3.4% of total U.S. utility-scale electricity (U.S. Energy Information Administration, 2024) Among US cities, Honolulu tops the chart, with per capita solar photovoltaic (PV) capacity installed at 1133 Watts DC/person in 2022. Washington DC was at 10th rank by 2022 (leaping up from 17th in 2018.) (Frontier Group, 2022)
The most likely reason behind this boost was DC’s Renewable Portfolio Standards (RPS) policies. RPS policies are regulatory mandates to increase the production of energy from renewable sources, requiring electric utilities to ensure that a specified percentage of the energy they generate or sell comes from renewable sources by a certain date. Despite the lack of a comprehensive federal RPS, state-level mandates have collectively contributed to a significant increase in the country’s renewable energy capacity, transforming the energy landscape and paving the way for a more sustainable future.
DC’s RPS is one of the country’s most ambitious, as underscored by the Clean Energy DC Omnibus Amendment Act of 2018. This act mandates that 100 percent of the District’s electric supply be sourced from renewable generators by 2032. Additionally, it sets forth a solar “carve-out” requirement that 5.5 percent of the 2032 electric supply must come from in-District solar, with this percentage increasing to 10 percent by 2041. This places the District as the third jurisdiction in the U.S., following Hawai’i and California, to establish a 100 percent renewable energy requirement.
The primary mechanism by which the DC Department of Energy and Environment (DOEE) influences the solar market is through Solar Renewable Energy Credits (SRECs) and Alternative Compliance Payments (ACP).
SRECs and ACP
SREC: Market-based instruments that represent the environmental benefits of generating solar power. Each SREC certifies that one megawatt-hour of solar electricity has been generated and can be sold or traded, providing an additional revenue stream for solar energy producers on top of the electricity they sell.
ACP: Penalties that electric utilities or other entities must pay if they fail to meet their renewable energy targets, specifically the portion of those targets that must be met through solar energy.
There have been several major policy changes in DC that affect solar adoption incentives, as summarized in this RPS report by the DC Public Service Commission (https://dcpsc.org/Orders-and-Regulations/PSC-Reports-to-the-DC-Council/Renewable-Energy-Portfolio-Standard.aspx) - Distributed Generation Amendment Act, 2011 - Amended RPS to increase solar requirements through 2023 to 2.5%. - Restricted eligible solar energy resources to those within the District or connected to a feeder serving the District.
Renewable Portfolio Standard Expansion Amendment Act, 2016
Increased RPS requirement to 50% by 2032, with solar energy requirement rising to 5%.
Modified solar energy RPS compliance fees, setting new rates from 2016 through 2032.
CleanEnergy DC Omnibus Amendment Act, 2018
Raised RPS to 100% by 2032, with solar energy requirement increasing to 5.5% by 2032 and 10% by 2041.
Extended solar REC banking period from three to five years.
Local Solar Expansion Amendment Act, 2022
Increased solar RPS requirement to 15% by 2041.
Directed funds from alternative compliance payments to support low-income ratepayer relief and solar policy studies.
Adjusted solar energy RPS compliance fees, gradually reducing them from 2024 through 2033, setting a fee of $0.30 per kWh from 2033 through 2041, and $0.10 thereafter.
Solar capacity installed has skyrocketed in DC.
Existing empirical evidence finds that US states that closed off their SREC markets to out-of-state facilites witnessed higher solar installation, as DC did in 2011. (Cohen et al 2022)
However, as the following animated graph shows, adoption really took off around 2016.
Sample gganimate code
# Animate the plotanim <- p +transition_reveal(date) +enter_grow() +# Animation for entering pointsexit_fade() +# Animation for exiting pointsease_aes('cubic-in-out') +# Animation speedlabs(title ='Year: {frame_along}', subtitle ="Tier-One Renewable Percent") # Update title with year# Render the animationanimate(anim, duration =10, fps =30, width =800, height =600, renderer =gifski_renderer())
Measuring actual generation or consumption, as opposed to capacity installed, is trickier. In 2022, the total retail electricity sales reported by suppliers in the District were 10.16 million MWh. To put this in perspective, Hawaii’s consumption in 2022 was around 9 million MWh, despite being 60 times larger in area and with 2x the population! (EIA 2022)
“The total number of SRECs retired for compliance was 263,919, which is equivalent to nearly 264,000 MWh and represents about 2.6% of the total retail electricity sales for the year”. CITE ‘DC PSC report’ Of course, SRECs can be held before they are cashed - it indicates that by this date, there has been at least 264,000 MWh generation of solar.
The DC Solar Market - Incentives and Costs
In Washington DC, the average solar panel costs $11,000-$14,000 after federal incentives
A household in DC can factor in three types of incentives available for a solar installation :
Federal rebates
SREC prices
Net metering
There is another type of indirect incentive, which might function as a ‘stick’ to the above ‘carrots’. This is the Building Energy Benchmarking Policy (BEPS), which aims to identify and impose financial penalties on large buildings (>10,000 sq feet) with greater than average energy consumption
The following figures describe the outlook for solar generation in the District:
Technical potential = 1200 MW
Target by 2040 = 665 MW
Adoption in 2023 = 201 MW
The above figures are from a report by Synapse Energy Economics, where it also lays out the factors holding back adoption in DC. For now, we zoom in on one particular factor laid out in the report:
“Given that 60 percent of households in the District are renter-occupied, home ownership and upfront costs are likely strong barriers to solar adoption”
Creating a building-level dataset to examine solar adoption
With the help of DC Open Data’s frequently updated and granular data on buildings, we can merge data solar and building attributes data for almost all of DC’s >100,000 residential buildings.
Building attributes, sourced from tax records properties in Washington, D.C. Contains data on building attributes.
Note: Merging properties with SSL
Linking these two datasets is more complex than a single merge. Solar records are given for an address, but CAMA records are linked to Square Suffix Lot (SSL) numbers, which identifies the plot of land on which a building is located. Some lots have multiple buildings, and some buildings/complexes span multiple lots.
The following table shows a small sample of the rows and columns in our resulting dataset.
SSL
STYLE_D
ROOF_D
NBHDNAME
cap_kw
max_sun_hours
min_dc_kwh
1679 0006
2 Story
Slate/Clay
American University
0
1176.855
1132.760
5626 0004
2 Story
Shingle
Hillcrest
0
1354.962
1300.345
3325 0012
2 Story
Metal- Sms
Petworth
4.8
1592.104
1539.959
3150 0048
2 Story
Metal- Sms
Petworth
3.12
1561.584
1521.092
0914 0033
2 Story
Metal- Sms
Old City 1
12.4
NA
NA
The fields max_sun_hours is the maximum hours of sunlight the building receives in a year, obtained from the Google Solar API.
Using Census data to check the home-ownership hypothesis
What does data from the American Community Survey tell us about the characteristics of high-solar adoption areas? Ideally, we would have joined building-level data of owner vs renter occupation, but in its absence, we can use block-group level data from the ACS 2019 on homeownership rates.
The above graph clearly shows that block groups with a higher share of homes inhabited by owners had higher per capita solar capacity, expressed as KiloWatts per 1000 people.
Spatial insights
The fact that the solar data has both the exact location and date of solar panel installation, added with DC Open Data’s rich repository of spatial building-level datasets implies that we can drill down at the hyperlocal level for insights in solar adoption patterns.
Check out this in-progress dashboard here!
Next steps:
Now that the data is consolidated, the next task is to model its diffusion process, and forecast it into the future.
A popular approach is NREL’s Distributed Renewable Generation model (dGen) model.